Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance.

Alvarez-Vargas, Daniela; Braithwaite, David; Lortie-Forgues, Hugues; Moore, Melody; Wan, Sirui; Martin, Elizabeth; Bailey, Drew Hal

Alvarez-Vargas, Daniela; Braithwaite, David; Lortie-Forgues, Hugues; Moore, Melody; Wan, Sirui; Martin, Elizabeth; Bailey, Drew Hal.

Afiliación

Alvarez-Vargas D; School of Education, University of California, Irvine, Irvine, California, United States of America.
Braithwaite D; Department of Psychology, Florida State University, Tallahassee, Florida, United States of America.
Lortie-Forgues H; Department of Mathematics Education, Loughborough University, Loughborough, Leicestershire, United Kingdom.
Moore M; Department of Psychology, Baylor University, Waco, Texas, United States of America.
Wan S; Department of Psychology at the University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Martin E; Department of Psychological Science, University of California, Irvine, Irvine, California, United States of America.
Bailey DH; Department of Psychological Science, University of California, Irvine, Irvine, California, United States of America.

PLoS One ; 18(10): e0286403, 2023.

Article en En | MEDLINE | ID: mdl-37883517

Asunto(s)

Docentes; Lenguaje; Humanos; Femenino; Masculino; Causalidad; Personal de Salud

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Docentes / Lenguaje Límite: Female / Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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